Motivated by the challenges associated with meaningfully relating different stochastic, microscopic models, here we apply methods from topological data analysis and computational geometry to quantify stripe patterns from two models of zebrafish at large scale. The two focal models of our case study—one off-lattice [1]} and one on-lattice [2]} individual-based model—are a useful place to center our work because they have largely the same cell interactions at their core from a biological perspective, but their computational implementation, cell density, and noise structure differ significantly. Drawing on these differences and similarities, we build on the methods [3]} to choose hyper-parameters in a way that ensures our comparisons are robust and fair. We also apply \(\alpha \) -shapes, a tool from computational geometry [4]}, [5]}, as a flexible means of identifying interstripe boundaries and computing curviness. Our work illustrates the challenges that arise when relating biologically similar—but mathematically and computationally different—stochastic models, and it shows how choices in the quantification process affect interpretations of results. This helps open the door to future large-scale studies of stochastic, spatial models, centered on questions including how sensitive model predictions are to implementation choices, and on how alternative mathematical perspectives can be combined to address biological questions.
